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Voting advice applications (VAAs) are online tools providing voting advice to their users. This voting advice is based on the match between the answers of the user and the answers of several political parties to a common questionnaire on political attitudes. To visualize this match, VAAs use a wide array of visualisations, most popular of which are the two-dimensional political maps. These maps show the position of both the political parties and the user in the political landscape, allowing the user to understand both their own position and their relation to the political parties. To construct these maps, VAAs require scales that represent the main underlying dimensions of the political space. This makes the correct construction of these scales important if the VAA aims to provide accurate and helpful voting advice. This paper presents three criteria that assess if a VAA achieves this aim. To illustrate their usefulness, these three criteria—unidimensionality, reliability and quality—are used to assess the scales in the cross-national EUVox VAA, a VAA designed for the European Parliament elections of 2014. Using techniques from Mokken scaling analysis and categorical principal component analysis to capture the metrics, I find that most scales show low unidimensionality and reliability. Moreover, even while designers can—and sometimes do—use certain techniques to improve their scales, these improvements are rarely enough to overcome all of the problems regarding unidimensionality, reliability and quality. This leaves certain problems for the designers of VAAs and designers of similar type online surveys. [ABSTRACT FROM AUTHOR]

While the design of voting advice applications (VAAs) is witnessing an increasing amount of attention, one aspect has until now been overlooked: its visualisations. This is remarkable, as it are those visualisations that communicate to the user the advice of the VAA. Therefore, this article aims to provide a first look at which visualisations VAAs adopt, why they adopt them, and how users comprehend them. For this, I will look at how design choices, specifically those on matching, influence the type of visualisation VAAs not only do but also have to, use. Second, I will report the results of a small-scale experiment that looked if all users comprehend similar visualisations in the same way. Here, I find that this is often not the case and that the interpretations of the users often differ. These first results suggest that VAA visualisations are wrongly underappreciated and demand closer attention of VAA designers. [ABSTRACT FROM AUTHOR]